Add model
Browse files- README.md +252 -0
- config.json +37 -0
- model.safetensors +3 -0
- pytorch_model.bin +3 -0
README.md
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| 1 |
+
---
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| 2 |
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tags:
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| 3 |
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- image-classification
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- timm
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library_tag: timm
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license: cc-by-nc-4.0
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datasets:
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| 8 |
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- imagenet-1k
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- ig-3.6b
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---
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# Model card for regnety_1280.swag_ft_in1k
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| 12 |
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A RegNetY-128GF image classification model. Pretrained according to SWAG: weakly-supervised learning on ~3.6B Instagram images and associated hashtags. Fine-tuned on ImageNet-1k by paper authors.
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These weights are restricted from commericial use by their CC-BY-NC-4.0 license.
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The `timm` RegNet implementation includes a number of enhancements not present in other implementations, including:
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* stochastic depth
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* gradient checkpointing
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* layer-wise LR decay
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* configurable output stride (dilation)
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* configurable activation and norm layers
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* option for a pre-activation bottleneck block used in RegNetV variant
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* only known RegNetZ model definitions with pretrained weights
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## Model Details
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| 28 |
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- **Model Type:** Image classification / feature backbone
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- **Model Stats:**
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| 30 |
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- Params (M): 644.8
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| 31 |
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- GMACs: 375.0
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- Activations (M): 210.2
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| 33 |
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- Image size: 384 x 384
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- **Papers:**
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- Revisiting Weakly Supervised Pre-Training of Visual Perception Models: https://arxiv.org/abs/2201.08371
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| 36 |
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- Designing Network Design Spaces: https://arxiv.org/abs/2003.13678
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| 37 |
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- **Original:** https://github.com/facebookresearch/SWAG
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| 38 |
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- **Dataset:** ImageNet-1k
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| 39 |
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- **Pretrain Dataset:** IG-3.6B
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| 40 |
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| 41 |
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## Model Usage
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| 42 |
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### Image Classification
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| 43 |
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```python
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| 44 |
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from urllib.request import urlopen
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| 45 |
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from PIL import Image
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| 46 |
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import timm
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| 47 |
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| 48 |
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img = Image.open(urlopen(
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| 49 |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
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| 50 |
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))
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| 51 |
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model = timm.create_model('regnety_1280.swag_ft_in1k', pretrained=True)
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| 53 |
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model = model.eval()
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| 54 |
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| 55 |
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# get model specific transforms (normalization, resize)
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| 56 |
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data_config = timm.data.resolve_model_data_config(model)
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| 57 |
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transforms = timm.data.create_transform(**data_config, is_training=False)
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| 58 |
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| 59 |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
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| 60 |
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| 61 |
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top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
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| 62 |
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```
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| 63 |
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| 64 |
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### Feature Map Extraction
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| 65 |
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```python
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| 66 |
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from urllib.request import urlopen
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| 67 |
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from PIL import Image
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| 68 |
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import timm
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| 69 |
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| 70 |
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img = Image.open(urlopen(
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| 71 |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
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| 72 |
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))
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| 73 |
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model = timm.create_model(
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'regnety_1280.swag_ft_in1k',
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| 76 |
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pretrained=True,
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| 77 |
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features_only=True,
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| 78 |
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)
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| 79 |
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model = model.eval()
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| 80 |
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| 81 |
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# get model specific transforms (normalization, resize)
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| 82 |
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data_config = timm.data.resolve_model_data_config(model)
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| 83 |
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transforms = timm.data.create_transform(**data_config, is_training=False)
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| 84 |
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| 85 |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
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| 86 |
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| 87 |
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for o in output:
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| 88 |
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# print shape of each feature map in output
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| 89 |
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# e.g.:
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| 90 |
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# torch.Size([1, 32, 192, 192])
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| 91 |
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# torch.Size([1, 528, 96, 96])
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| 92 |
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# torch.Size([1, 1056, 48, 48])
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| 93 |
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# torch.Size([1, 2904, 24, 24])
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| 94 |
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# torch.Size([1, 7392, 12, 12])
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| 95 |
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| 96 |
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print(o.shape)
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| 97 |
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```
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| 98 |
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| 99 |
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### Image Embeddings
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| 100 |
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```python
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| 101 |
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from urllib.request import urlopen
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| 102 |
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from PIL import Image
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| 103 |
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import timm
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| 104 |
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| 105 |
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img = Image.open(urlopen(
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| 106 |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
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| 107 |
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))
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| 108 |
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| 109 |
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model = timm.create_model(
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| 110 |
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'regnety_1280.swag_ft_in1k',
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| 111 |
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pretrained=True,
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| 112 |
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num_classes=0, # remove classifier nn.Linear
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| 113 |
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)
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| 114 |
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model = model.eval()
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| 115 |
+
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| 116 |
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# get model specific transforms (normalization, resize)
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| 117 |
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data_config = timm.data.resolve_model_data_config(model)
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| 118 |
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transforms = timm.data.create_transform(**data_config, is_training=False)
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| 119 |
+
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| 120 |
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output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
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| 121 |
+
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| 122 |
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# or equivalently (without needing to set num_classes=0)
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| 123 |
+
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| 124 |
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output = model.forward_features(transforms(img).unsqueeze(0))
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| 125 |
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# output is unpooled, a (1, 7392, 12, 12) shaped tensor
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| 126 |
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| 127 |
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output = model.forward_head(output, pre_logits=True)
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| 128 |
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# output is a (1, num_features) shaped tensor
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| 129 |
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```
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| 130 |
+
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| 131 |
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## Model Comparison
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| 132 |
+
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
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| 133 |
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| 134 |
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For the comparison summary below, the ra_in1k, ra3_in1k, ch_in1k, sw_*, and lion_* tagged weights are trained in `timm`.
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| 135 |
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| 136 |
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|model |img_size|top1 |top5 |param_count|gmacs|macts |
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| 137 |
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|-------------------------|--------|------|------|-----------|-----|------|
|
| 138 |
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|[regnety_1280.swag_ft_in1k](https://huggingface.co/timm/regnety_1280.swag_ft_in1k)|384 |88.228|98.684|644.81 |374.99|210.2 |
|
| 139 |
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|[regnety_320.swag_ft_in1k](https://huggingface.co/timm/regnety_320.swag_ft_in1k)|384 |86.84 |98.364|145.05 |95.0 |88.87 |
|
| 140 |
+
|[regnety_160.swag_ft_in1k](https://huggingface.co/timm/regnety_160.swag_ft_in1k)|384 |86.024|98.05 |83.59 |46.87|67.67 |
|
| 141 |
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|[regnety_160.sw_in12k_ft_in1k](https://huggingface.co/timm/regnety_160.sw_in12k_ft_in1k)|288 |86.004|97.83 |83.59 |26.37|38.07 |
|
| 142 |
+
|[regnety_1280.swag_lc_in1k](https://huggingface.co/timm/regnety_1280.swag_lc_in1k)|224 |85.996|97.848|644.81 |127.66|71.58 |
|
| 143 |
+
|[regnety_160.lion_in12k_ft_in1k](https://huggingface.co/timm/regnety_160.lion_in12k_ft_in1k)|288 |85.982|97.844|83.59 |26.37|38.07 |
|
| 144 |
+
|[regnety_160.sw_in12k_ft_in1k](https://huggingface.co/timm/regnety_160.sw_in12k_ft_in1k)|224 |85.574|97.666|83.59 |15.96|23.04 |
|
| 145 |
+
|[regnety_160.lion_in12k_ft_in1k](https://huggingface.co/timm/regnety_160.lion_in12k_ft_in1k)|224 |85.564|97.674|83.59 |15.96|23.04 |
|
| 146 |
+
|[regnety_120.sw_in12k_ft_in1k](https://huggingface.co/timm/regnety_120.sw_in12k_ft_in1k)|288 |85.398|97.584|51.82 |20.06|35.34 |
|
| 147 |
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|[regnety_2560.seer_ft_in1k](https://huggingface.co/timm/regnety_2560.seer_ft_in1k)|384 |85.15 |97.436|1282.6 |747.83|296.49|
|
| 148 |
+
|[regnetz_e8.ra3_in1k](https://huggingface.co/timm/regnetz_e8.ra3_in1k)|320 |85.036|97.268|57.7 |15.46|63.94 |
|
| 149 |
+
|[regnety_120.sw_in12k_ft_in1k](https://huggingface.co/timm/regnety_120.sw_in12k_ft_in1k)|224 |84.976|97.416|51.82 |12.14|21.38 |
|
| 150 |
+
|[regnety_320.swag_lc_in1k](https://huggingface.co/timm/regnety_320.swag_lc_in1k)|224 |84.56 |97.446|145.05 |32.34|30.26 |
|
| 151 |
+
|[regnetz_040_h.ra3_in1k](https://huggingface.co/timm/regnetz_040_h.ra3_in1k)|320 |84.496|97.004|28.94 |6.43 |37.94 |
|
| 152 |
+
|[regnetz_e8.ra3_in1k](https://huggingface.co/timm/regnetz_e8.ra3_in1k)|256 |84.436|97.02 |57.7 |9.91 |40.94 |
|
| 153 |
+
|[regnety_1280.seer_ft_in1k](https://huggingface.co/timm/regnety_1280.seer_ft_in1k)|384 |84.432|97.092|644.81 |374.99|210.2 |
|
| 154 |
+
|[regnetz_040.ra3_in1k](https://huggingface.co/timm/regnetz_040.ra3_in1k)|320 |84.246|96.93 |27.12 |6.35 |37.78 |
|
| 155 |
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|[regnetz_d8.ra3_in1k](https://huggingface.co/timm/regnetz_d8.ra3_in1k)|320 |84.054|96.992|23.37 |6.19 |37.08 |
|
| 156 |
+
|[regnetz_d8_evos.ch_in1k](https://huggingface.co/timm/regnetz_d8_evos.ch_in1k)|320 |84.038|96.992|23.46 |7.03 |38.92 |
|
| 157 |
+
|[regnetz_d32.ra3_in1k](https://huggingface.co/timm/regnetz_d32.ra3_in1k)|320 |84.022|96.866|27.58 |9.33 |37.08 |
|
| 158 |
+
|[regnety_080.ra3_in1k](https://huggingface.co/timm/regnety_080.ra3_in1k)|288 |83.932|96.888|39.18 |13.22|29.69 |
|
| 159 |
+
|[regnety_640.seer_ft_in1k](https://huggingface.co/timm/regnety_640.seer_ft_in1k)|384 |83.912|96.924|281.38 |188.47|124.83|
|
| 160 |
+
|[regnety_160.swag_lc_in1k](https://huggingface.co/timm/regnety_160.swag_lc_in1k)|224 |83.778|97.286|83.59 |15.96|23.04 |
|
| 161 |
+
|[regnetz_040_h.ra3_in1k](https://huggingface.co/timm/regnetz_040_h.ra3_in1k)|256 |83.776|96.704|28.94 |4.12 |24.29 |
|
| 162 |
+
|[regnetv_064.ra3_in1k](https://huggingface.co/timm/regnetv_064.ra3_in1k)|288 |83.72 |96.75 |30.58 |10.55|27.11 |
|
| 163 |
+
|[regnety_064.ra3_in1k](https://huggingface.co/timm/regnety_064.ra3_in1k)|288 |83.718|96.724|30.58 |10.56|27.11 |
|
| 164 |
+
|[regnety_160.deit_in1k](https://huggingface.co/timm/regnety_160.deit_in1k)|288 |83.69 |96.778|83.59 |26.37|38.07 |
|
| 165 |
+
|[regnetz_040.ra3_in1k](https://huggingface.co/timm/regnetz_040.ra3_in1k)|256 |83.62 |96.704|27.12 |4.06 |24.19 |
|
| 166 |
+
|[regnetz_d8.ra3_in1k](https://huggingface.co/timm/regnetz_d8.ra3_in1k)|256 |83.438|96.776|23.37 |3.97 |23.74 |
|
| 167 |
+
|[regnetz_d32.ra3_in1k](https://huggingface.co/timm/regnetz_d32.ra3_in1k)|256 |83.424|96.632|27.58 |5.98 |23.74 |
|
| 168 |
+
|[regnetz_d8_evos.ch_in1k](https://huggingface.co/timm/regnetz_d8_evos.ch_in1k)|256 |83.36 |96.636|23.46 |4.5 |24.92 |
|
| 169 |
+
|[regnety_320.seer_ft_in1k](https://huggingface.co/timm/regnety_320.seer_ft_in1k)|384 |83.35 |96.71 |145.05 |95.0 |88.87 |
|
| 170 |
+
|[regnetv_040.ra3_in1k](https://huggingface.co/timm/regnetv_040.ra3_in1k)|288 |83.204|96.66 |20.64 |6.6 |20.3 |
|
| 171 |
+
|[regnety_320.tv2_in1k](https://huggingface.co/timm/regnety_320.tv2_in1k)|224 |83.162|96.42 |145.05 |32.34|30.26 |
|
| 172 |
+
|[regnety_080.ra3_in1k](https://huggingface.co/timm/regnety_080.ra3_in1k)|224 |83.16 |96.486|39.18 |8.0 |17.97 |
|
| 173 |
+
|[regnetv_064.ra3_in1k](https://huggingface.co/timm/regnetv_064.ra3_in1k)|224 |83.108|96.458|30.58 |6.39 |16.41 |
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| 174 |
+
|[regnety_040.ra3_in1k](https://huggingface.co/timm/regnety_040.ra3_in1k)|288 |83.044|96.5 |20.65 |6.61 |20.3 |
|
| 175 |
+
|[regnety_064.ra3_in1k](https://huggingface.co/timm/regnety_064.ra3_in1k)|224 |83.02 |96.292|30.58 |6.39 |16.41 |
|
| 176 |
+
|[regnety_160.deit_in1k](https://huggingface.co/timm/regnety_160.deit_in1k)|224 |82.974|96.502|83.59 |15.96|23.04 |
|
| 177 |
+
|[regnetx_320.tv2_in1k](https://huggingface.co/timm/regnetx_320.tv2_in1k)|224 |82.816|96.208|107.81 |31.81|36.3 |
|
| 178 |
+
|[regnety_032.ra_in1k](https://huggingface.co/timm/regnety_032.ra_in1k)|288 |82.742|96.418|19.44 |5.29 |18.61 |
|
| 179 |
+
|[regnety_160.tv2_in1k](https://huggingface.co/timm/regnety_160.tv2_in1k)|224 |82.634|96.22 |83.59 |15.96|23.04 |
|
| 180 |
+
|[regnetz_c16_evos.ch_in1k](https://huggingface.co/timm/regnetz_c16_evos.ch_in1k)|320 |82.634|96.472|13.49 |3.86 |25.88 |
|
| 181 |
+
|[regnety_080_tv.tv2_in1k](https://huggingface.co/timm/regnety_080_tv.tv2_in1k)|224 |82.592|96.246|39.38 |8.51 |19.73 |
|
| 182 |
+
|[regnetx_160.tv2_in1k](https://huggingface.co/timm/regnetx_160.tv2_in1k)|224 |82.564|96.052|54.28 |15.99|25.52 |
|
| 183 |
+
|[regnetz_c16.ra3_in1k](https://huggingface.co/timm/regnetz_c16.ra3_in1k)|320 |82.51 |96.358|13.46 |3.92 |25.88 |
|
| 184 |
+
|[regnetv_040.ra3_in1k](https://huggingface.co/timm/regnetv_040.ra3_in1k)|224 |82.44 |96.198|20.64 |4.0 |12.29 |
|
| 185 |
+
|[regnety_040.ra3_in1k](https://huggingface.co/timm/regnety_040.ra3_in1k)|224 |82.304|96.078|20.65 |4.0 |12.29 |
|
| 186 |
+
|[regnetz_c16.ra3_in1k](https://huggingface.co/timm/regnetz_c16.ra3_in1k)|256 |82.16 |96.048|13.46 |2.51 |16.57 |
|
| 187 |
+
|[regnetz_c16_evos.ch_in1k](https://huggingface.co/timm/regnetz_c16_evos.ch_in1k)|256 |81.936|96.15 |13.49 |2.48 |16.57 |
|
| 188 |
+
|[regnety_032.ra_in1k](https://huggingface.co/timm/regnety_032.ra_in1k)|224 |81.924|95.988|19.44 |3.2 |11.26 |
|
| 189 |
+
|[regnety_032.tv2_in1k](https://huggingface.co/timm/regnety_032.tv2_in1k)|224 |81.77 |95.842|19.44 |3.2 |11.26 |
|
| 190 |
+
|[regnetx_080.tv2_in1k](https://huggingface.co/timm/regnetx_080.tv2_in1k)|224 |81.552|95.544|39.57 |8.02 |14.06 |
|
| 191 |
+
|[regnetx_032.tv2_in1k](https://huggingface.co/timm/regnetx_032.tv2_in1k)|224 |80.924|95.27 |15.3 |3.2 |11.37 |
|
| 192 |
+
|[regnety_320.pycls_in1k](https://huggingface.co/timm/regnety_320.pycls_in1k)|224 |80.804|95.246|145.05 |32.34|30.26 |
|
| 193 |
+
|[regnetz_b16.ra3_in1k](https://huggingface.co/timm/regnetz_b16.ra3_in1k)|288 |80.712|95.47 |9.72 |2.39 |16.43 |
|
| 194 |
+
|[regnety_016.tv2_in1k](https://huggingface.co/timm/regnety_016.tv2_in1k)|224 |80.66 |95.334|11.2 |1.63 |8.04 |
|
| 195 |
+
|[regnety_120.pycls_in1k](https://huggingface.co/timm/regnety_120.pycls_in1k)|224 |80.37 |95.12 |51.82 |12.14|21.38 |
|
| 196 |
+
|[regnety_160.pycls_in1k](https://huggingface.co/timm/regnety_160.pycls_in1k)|224 |80.288|94.964|83.59 |15.96|23.04 |
|
| 197 |
+
|[regnetx_320.pycls_in1k](https://huggingface.co/timm/regnetx_320.pycls_in1k)|224 |80.246|95.01 |107.81 |31.81|36.3 |
|
| 198 |
+
|[regnety_080.pycls_in1k](https://huggingface.co/timm/regnety_080.pycls_in1k)|224 |79.882|94.834|39.18 |8.0 |17.97 |
|
| 199 |
+
|[regnetz_b16.ra3_in1k](https://huggingface.co/timm/regnetz_b16.ra3_in1k)|224 |79.872|94.974|9.72 |1.45 |9.95 |
|
| 200 |
+
|[regnetx_160.pycls_in1k](https://huggingface.co/timm/regnetx_160.pycls_in1k)|224 |79.862|94.828|54.28 |15.99|25.52 |
|
| 201 |
+
|[regnety_064.pycls_in1k](https://huggingface.co/timm/regnety_064.pycls_in1k)|224 |79.716|94.772|30.58 |6.39 |16.41 |
|
| 202 |
+
|[regnetx_120.pycls_in1k](https://huggingface.co/timm/regnetx_120.pycls_in1k)|224 |79.592|94.738|46.11 |12.13|21.37 |
|
| 203 |
+
|[regnetx_016.tv2_in1k](https://huggingface.co/timm/regnetx_016.tv2_in1k)|224 |79.44 |94.772|9.19 |1.62 |7.93 |
|
| 204 |
+
|[regnety_040.pycls_in1k](https://huggingface.co/timm/regnety_040.pycls_in1k)|224 |79.23 |94.654|20.65 |4.0 |12.29 |
|
| 205 |
+
|[regnetx_080.pycls_in1k](https://huggingface.co/timm/regnetx_080.pycls_in1k)|224 |79.198|94.55 |39.57 |8.02 |14.06 |
|
| 206 |
+
|[regnetx_064.pycls_in1k](https://huggingface.co/timm/regnetx_064.pycls_in1k)|224 |79.064|94.454|26.21 |6.49 |16.37 |
|
| 207 |
+
|[regnety_032.pycls_in1k](https://huggingface.co/timm/regnety_032.pycls_in1k)|224 |78.884|94.412|19.44 |3.2 |11.26 |
|
| 208 |
+
|[regnety_008_tv.tv2_in1k](https://huggingface.co/timm/regnety_008_tv.tv2_in1k)|224 |78.654|94.388|6.43 |0.84 |5.42 |
|
| 209 |
+
|[regnetx_040.pycls_in1k](https://huggingface.co/timm/regnetx_040.pycls_in1k)|224 |78.482|94.24 |22.12 |3.99 |12.2 |
|
| 210 |
+
|[regnetx_032.pycls_in1k](https://huggingface.co/timm/regnetx_032.pycls_in1k)|224 |78.178|94.08 |15.3 |3.2 |11.37 |
|
| 211 |
+
|[regnety_016.pycls_in1k](https://huggingface.co/timm/regnety_016.pycls_in1k)|224 |77.862|93.73 |11.2 |1.63 |8.04 |
|
| 212 |
+
|[regnetx_008.tv2_in1k](https://huggingface.co/timm/regnetx_008.tv2_in1k)|224 |77.302|93.672|7.26 |0.81 |5.15 |
|
| 213 |
+
|[regnetx_016.pycls_in1k](https://huggingface.co/timm/regnetx_016.pycls_in1k)|224 |76.908|93.418|9.19 |1.62 |7.93 |
|
| 214 |
+
|[regnety_008.pycls_in1k](https://huggingface.co/timm/regnety_008.pycls_in1k)|224 |76.296|93.05 |6.26 |0.81 |5.25 |
|
| 215 |
+
|[regnety_004.tv2_in1k](https://huggingface.co/timm/regnety_004.tv2_in1k)|224 |75.592|92.712|4.34 |0.41 |3.89 |
|
| 216 |
+
|[regnety_006.pycls_in1k](https://huggingface.co/timm/regnety_006.pycls_in1k)|224 |75.244|92.518|6.06 |0.61 |4.33 |
|
| 217 |
+
|[regnetx_008.pycls_in1k](https://huggingface.co/timm/regnetx_008.pycls_in1k)|224 |75.042|92.342|7.26 |0.81 |5.15 |
|
| 218 |
+
|[regnetx_004_tv.tv2_in1k](https://huggingface.co/timm/regnetx_004_tv.tv2_in1k)|224 |74.57 |92.184|5.5 |0.42 |3.17 |
|
| 219 |
+
|[regnety_004.pycls_in1k](https://huggingface.co/timm/regnety_004.pycls_in1k)|224 |74.018|91.764|4.34 |0.41 |3.89 |
|
| 220 |
+
|[regnetx_006.pycls_in1k](https://huggingface.co/timm/regnetx_006.pycls_in1k)|224 |73.862|91.67 |6.2 |0.61 |3.98 |
|
| 221 |
+
|[regnetx_004.pycls_in1k](https://huggingface.co/timm/regnetx_004.pycls_in1k)|224 |72.38 |90.832|5.16 |0.4 |3.14 |
|
| 222 |
+
|[regnety_002.pycls_in1k](https://huggingface.co/timm/regnety_002.pycls_in1k)|224 |70.282|89.534|3.16 |0.2 |2.17 |
|
| 223 |
+
|[regnetx_002.pycls_in1k](https://huggingface.co/timm/regnetx_002.pycls_in1k)|224 |68.752|88.556|2.68 |0.2 |2.16 |
|
| 224 |
+
|
| 225 |
+
## Citation
|
| 226 |
+
```bibtex
|
| 227 |
+
@inproceedings{singh2022revisiting,
|
| 228 |
+
title={{Revisiting Weakly Supervised Pre-Training of Visual Perception Models}},
|
| 229 |
+
author={Singh, Mannat and Gustafson, Laura and Adcock, Aaron and Reis, Vinicius de Freitas and Gedik, Bugra and Kosaraju, Raj Prateek and Mahajan, Dhruv and Girshick, Ross and Doll{'a}r, Piotr and van der Maaten, Laurens},
|
| 230 |
+
booktitle={CVPR},
|
| 231 |
+
year={2022}
|
| 232 |
+
}
|
| 233 |
+
```
|
| 234 |
+
```bibtex
|
| 235 |
+
@InProceedings{Radosavovic2020,
|
| 236 |
+
title = {Designing Network Design Spaces},
|
| 237 |
+
author = {Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Doll{'a}r},
|
| 238 |
+
booktitle = {CVPR},
|
| 239 |
+
year = {2020}
|
| 240 |
+
}
|
| 241 |
+
```
|
| 242 |
+
```bibtex
|
| 243 |
+
@misc{rw2019timm,
|
| 244 |
+
author = {Ross Wightman},
|
| 245 |
+
title = {PyTorch Image Models},
|
| 246 |
+
year = {2019},
|
| 247 |
+
publisher = {GitHub},
|
| 248 |
+
journal = {GitHub repository},
|
| 249 |
+
doi = {10.5281/zenodo.4414861},
|
| 250 |
+
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
|
| 251 |
+
}
|
| 252 |
+
```
|
config.json
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architecture": "regnety_1280",
|
| 3 |
+
"num_classes": 1000,
|
| 4 |
+
"num_features": 7392,
|
| 5 |
+
"pretrained_cfg": {
|
| 6 |
+
"tag": "swag_ft_in1k",
|
| 7 |
+
"custom_load": false,
|
| 8 |
+
"input_size": [
|
| 9 |
+
3,
|
| 10 |
+
384,
|
| 11 |
+
384
|
| 12 |
+
],
|
| 13 |
+
"fixed_input_size": false,
|
| 14 |
+
"interpolation": "bicubic",
|
| 15 |
+
"crop_pct": 1.0,
|
| 16 |
+
"crop_mode": "center",
|
| 17 |
+
"mean": [
|
| 18 |
+
0.485,
|
| 19 |
+
0.456,
|
| 20 |
+
0.406
|
| 21 |
+
],
|
| 22 |
+
"std": [
|
| 23 |
+
0.229,
|
| 24 |
+
0.224,
|
| 25 |
+
0.225
|
| 26 |
+
],
|
| 27 |
+
"num_classes": 1000,
|
| 28 |
+
"pool_size": [
|
| 29 |
+
12,
|
| 30 |
+
12
|
| 31 |
+
],
|
| 32 |
+
"first_conv": "stem.conv",
|
| 33 |
+
"classifier": "head.fc",
|
| 34 |
+
"license": "cc-by-nc-4.0",
|
| 35 |
+
"origin_url": "https://github.com/pytorch/vision"
|
| 36 |
+
}
|
| 37 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:87cea96f9242b6c47da61d8858021bc695f231d822f09f2d862126fc6a16738d
|
| 3 |
+
size 2580972016
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7a1e19fe5105ffc313bbf02345648aae4c67561e37a95e573b57598d363f09c7
|
| 3 |
+
size 2581122197
|